Deep learning-based differentiation of non-tuberculous mycobacterial lung disease and pulmonary tuberculosis using chest CT

Background: Differentiating non-tuberculous mycobacterial lung disease (NTM-LD) from pulmonary tuberculosis (PTB) remains a significant clinical challenge owing to their overlapping clinical and imaging features, despite markedly different therapeutic strategies. Methods: = 80; NTM-LD: 41, PTB: 39). After lung segmentation with nnU-Net, images were intensity-normalized and resampled to 256 × 256 × 128 voxels. A 3D ResNeXt-based classifier was developed and compared against six mainstream deep learning architectures: ResNet, SENet, DenseNet, ShuffleNet, Transformer, and Swin Transformer. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and F1 score. Results: < 0.05). Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations highlighted disease-specific features, including nodular bronchiectasis and tree-in-bud opacities in NTM-LD, as well as thick-walled cavitary lesions in PTB. Conclusion: The 3D ResNeXt model demonstrated superior and interpretable performance in differentiating NTM-LD from PTB on chest CT. It holds promise as a valuable clinical decision-support tool, although prospective multicenter validation is warranted.